DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration

Abstract

This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Such point clouds originate, for example, from consecutive measurements of a LiDAR mounted on a moving platform. The main difficulty in deep registration of raw point clouds is the fusion of template and source point cloud. Our proposed architecture applies flow embedding to tackle this problem, which generates features that describe the motion of each template point. These features are then used to predict the alignment in an end-to-end fashion without extracting explicit point correspondences between both input clouds. We rely on the KITTI odometry and ModelNet40 datasets for evaluating our method on various point distributions. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.

arXiv comment7 pages, 5 figures, 4 tables

References in corpus (3)

• Open3D: A Modern Library for 3D Data Processing
• 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
• DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

Cited by in corpus (5)

• Point Transformer
• LiDAR Odometry Methodologies for Autonomous Driving: A Survey
• SynBench: A Synthetic Benchmark for Non-rigid 3D Point Cloud Registration
• Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks
• PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization